Applied Sports Science newsletter – January 1, 2018

As he approaches his 33rd birthday in his remarkable 15th NBA season, LeBron James has found himself echoing his old Nike marketing slogan to gently remind people tuning in what they’re witnessing.

“I think people have just grown accustomed of what I do, and it gets taken for granted at times what I do because I do it so often and it’s been a constant thing for so long,” James said Dec. 6 on the morning of the Cleveland Cavaliers’ game against the Sacramento Kings. “It’s like, ‘Oh, that’s what LeBron’s supposed to do.’ It looks easy, but it’s not.”

Andrew Luck flew 4,152 miles to the Netherlands to have physical therapy on his shoulder. That’s what he said Friday. That’s what he wants you to believe.

Do you believe?

Just rehab, he said. Nothing crazy, no injections. Nothing out of the ordinary. That’s what he said.

“We just rehabbed at a clinic in the Netherlands,” he said Friday, a quote I’m including here because it’s important that, if you believe anybody in the story, you believe me.

… injuries like Hayward’s often rip open an emotional wound. Seconds after Hayward went down, Livingston’s phone inevitably blew up with the news and photos of the injury. But the Golden State guard couldn’t bring himself to watch the clip.

“I don’t watch those plays anymore,” Livingston told B/R Mag. “The eyes are the windows into the soul. It’s just a mental standpoint about not putting that stuff into your head.”

Livingston can relate to the road ahead for Hayward. Indeed, the physical pain can be torturous at times, but Livingston is quick to point out that the loneliness and emotional disconnect can be just as overwhelming.

… “Look, they pulled out (of warm-up tournaments) maybe because they’re not quite ready yet, or maybe because they need a couple more weeks,” Federer said.

“We’ll only really know in a week or 10 days’ time who is really actually going to pull out. That’s when you can really talk about it.

“Because so far maybe it’s precautionary. Maybe it’s really that they’re not feeling well.

“I hope they all get back. But something tells me that two guys out of the five, six probably won’t make it. Because it seems too many guys are actually fighting something.”

… new science has finally given us an answer to the mystery of sleep. In 2013, Danish scientists testing mice found that as animals sleep, their brains actually compress and grow smaller. Roughly 75% of our brain mass is water weight in the form of blood. Our brains receive energy from blood, so when the blood is pumping, our brains are plump. As we sleep, various parts of our brain shut down, and that reduced power consumption reduces the swelling across our neurons. The brain’s blood retreats, creating large empty spaces.

It is what happens next in the brain that makes sleep so vital to our survival. Our bodies leverage something called the lymphatic system to push out toxins. The lymphatic system mimics the flow of blood throughout our entire body and works to remove waste and byproducts as we consume energy. Blood is filled with nasty toxins, but our bodies take the good, and the lymphatic system excretes the bad. The lymphatic system works throughout the entire body with one exception: the brain.

Nearly a decade ago, Arsene Wenger ranked the Paris region as the second-best talent pool in soccer after Sao Paulo in Brazil. But by now, the French capital surely ranks top.

Here are just a few of today’s players raised in Greater Paris: Paul Pogba, Anthony Martial, N’Golo Kante, Kingsley Coman, Blaise Matuidi and Kylian Mbappe, plus three other regular Paris Saint-Germain starters, the Algerian internationals Riyad Mahrez and Yacine Brahimi, and various Senegalese and Moroccan internationals who will play at the coming World Cup. In fact, the Ile-de-France (as Greater Paris is known) probably produces more talent than Asia, Africa and North America combined. Why?

Managers increasingly work with teams across geographic distance, or with varying disciplinary expertise, or involving complicated hierarchies of power. Leading this kind of “extreme teaming” requires management skills that don’t always come naturally — such as humility.

Background

The associations between internal and external measures of training load and intensity are important in understanding the training process and the validity of specific internal measures.
Objectives

We aimed to provide meta-analytic estimates of the relationships, as determined by a correlation coefficient, between internal and external measures of load and intensity during team-sport training and competition. A further aim was to examine the moderating effects of training mode on these relationships.
Methods

We searched six electronic databases (Scopus, Web of Science, PubMed, MEDLINE, SPORTDiscus, CINAHL) for original research articles published up to September 2017. A Boolean search phrase was created to include search terms relevant to team-sport athletes (population; 37 keywords), internal load (dependent variable; 35 keywords), and external load (independent variable; 81 keywords). Articles were considered for meta-analysis when a correlation coefficient describing the association between at least one internal and one external measure of session load or intensity, measured in the time or frequency domain, was obtained from team-sport athletes during normal training or match-play (i.e., unstructured observational study). The final data sample included 122 estimates from 13 independent studies describing 15 unique relationships between three internal and nine external measures of load and intensity. This sample included 295 athletes and 10,418 individual session observations. Internal measures were session ratings of perceived exertion (sRPE), sRPE training load (sRPE-TL), and heart-rate-derived training impulse (TRIMP). External measures were total distance (TD), the distance covered at high and very high speeds (HSRD ≥ 13.1–15.0 km h−1 and VHSRD ≥ 16.9–19.8 km h−1, respectively), accelerometer load (AL), and the number of sustained impacts (Impacts > 2–5 G). Distinct training modes were identified as either mixed (reference condition), skills, metabolic, or neuromuscular. Separate random effects meta-analyses were conducted for each dataset (n = 15) to determine the pooled relationships between internal and external measures of load and intensity. The moderating effects of training mode were examined using random-effects meta-regression for datasets with at least ten estimates (n = 4). Magnitude-based inferences were used to interpret analyses outcomes.
Results

During all training modes combined, the external load relationships for sRPE-TL were possibly very large with TD [r = 0.79; 90% confidence interval (CI) 0.74 to 0.83], possibly large with AL (r = 0.63; 90% CI 0.54 to 0.70) and Impacts (r = 0.57; 90% CI 0.47 to 0.64), and likely moderate with HSRD (r = 0.47; 90% CI 0.32 to 0.59). The relationship between TRIMP and AL was possibly large (r = 0.54; 90% CI 0.40 to 0.66). All other relationships were unclear or not possible to infer (r range 0.17–0.74, n = 10 datasets). Between-estimate heterogeneity [standard deviations (SDs) representing unexplained variation; τ] in the pooled internal–external relationships were trivial to extremely large for sRPE (τ range = 0.00–0.47), small to large for sRPE-TL (τ range = 0.07–0.31), and trivial to moderate for TRIMP (τ range= 0.00–0.17). The internal–external load relationships during mixed training were possibly very large for sRPE-TL with TD (r = 0.82; 90% CI 0.75 to 0.87) and AL (r = 0.81; 90% CI 0.74 to 0.86), and TRIMP with AL (r = 0.72; 90% CI 0.55 to 0.84), and possibly large for sRPE-TL with HSRD (r = 0.65; 90% CI 0.44 to 0.80). A reduction in these correlation magnitudes was evident for all other training modes (range of the change in r when compared with mixed training − 0.08 to − 0.58), with these differences being unclear to possibly large. Training mode explained 24–100% of the between-estimate variance in the internal–external load relationships.
Conclusion

Measures of internal load derived from perceived exertion and heart rate show consistently positive associations with running- and accelerometer-derived external loads and intensity during team-sport training and competition, but the magnitude and uncertainty of these relationships are measure and training mode dependent.

… In a new paper appearing in IEEE Access, members of the Camera Culture group present a new approach to time-of-flight imaging that increases its depth resolution 1,000-fold. That’s the type of resolution that could make self-driving cars practical.

The new approach could also enable accurate distance measurements through fog, which has proven to be a major obstacle to the development of self-driving cars.

At a range of 2 meters, existing time-of-flight systems have a depth resolution of about a centimeter. That’s good enough for the assisted-parking and collision-detection systems on today’s cars.

To see color, the human visual system combines the response of three types of cone cells in the retina – a compressive process that discards a significant amount of spectral information. Here, we present an approach to enhance human color vision by breaking its inherent binocular redundancy, providing different spectral content to each eye. We fabricated and tested a set of optical filters that “splits” the response of the short-wavelength cone between the two eyes in individuals with typical trichromatic vision, simulating the presence of approximately four distinct cone types (“tetrachromacy”). Such an increase in the number of simulated cone types can reduce the prevalence of metamers – pairs of distinct spectra that resolve to the same perceived color. This technique may result in an enhancement of spectral perception, with applications ranging from camouflage detection and anti-counterfeiting to new types of artwork and data visualization.

Background:

Several studies have shown that foot posture is related to the incidence of ankle sprains in athletes and in nonathletic populations, but this association has not previously been considered in basketball players. This study investigates the relationship between foot posture and lower limb injuries in elite basketball players.

Design and Method:

Two hundred twenty participants were recruited as a convenience sample. The players had a mean age of 22.51 ± 3.88 years and a body mass index of 23.98 ± 1.80. The players’ medical records were accessed from the preceding 10 years, and injuries were recorded according to their location (knee, foot, and/or ankle). In addition, the Foot Posture Index (FPI) was scored for each player, and their playing positions were noted.

Results:

An average FPI score of 2.66 was obtained across all players, with guards presenting a significantly lower average FPI of −0.48 (P < 0.001) compared with the rest of playing positions, indicating a more supinated foot. However, center players presented an average FPI of 5.15 (P < 0.001), indicating a more pronated foot. The most common injuries observed were lateral ankle sprain (n = 214) and patellar tendinopathy (n = 126). Patellar tendinopathy was more common in supinated feet (30.08%) compared with 20.7% and 19.8% in pronated and neutral feet, respectively. Conclusions:

The most common lower limb injuries observed in basketball players were lateral ankle sprain and patellar tendinopathy. Patellar tendinopathy was more commonly associated with the supinated feet. Guard players tended to have a more supinated foot, whereas centers presented a more pronated foot.

… At any given time, an average of 43% [95% CI: 37-49%] of the elite sport athletes had some form of health problem and 25% [20-31%] had substantial health problems. The prevalence of health problems was similar between the elite team sport athletes versus their teammates, except for substantial injuries (22% [16-30%] vs. 10% [5-20%]). Endurance sport athletes reported more illnesses (23% [15-35%]) than technical and team sport athletes (10% [5-20%] and 8% [4-14%]). In contrast, technical and team sport athletes reported more injuries (36% [95% CI: 25-48] and 37% [95% CI 29-45]) compared to endurance sport athletes (15% [8-25%]). The total impact of health problems was roughly split in thirds between overuse injuries (37%), acute injuries (34%) and illnesses (30%).

This is the first prospective study to present self-reported injury and illness data in a large heterogeneous group of youth elite athletes, documenting a substantial impact of both injuries and illnesses on the health of this population.

… in the walled-off world of health care, with its HIPAA laws and privacy hot buttons, AI is only just beginning to change the way doctors see, diagnose, treat, and monitor patients. The potential to save lives and money is tremendous; one report estimates big data-crunching algorithms could save medicine and pharma up to $100 billion a year, as a result of AI-assisted efficiencies in clinical trials, research, and decision-making in the doctor’s office. Which is why tech titans like IBM, Microsoft, Google, and Apple are spinning up their own AI health care pet projects. And why every health-focused startup pitching Silicon Valley VCs throws in a “machine learning” or “deep neural net” for good measure.

These algorithms get better the more data they see. And health data is practically hemorrhaging out of mobile devices, wearables, and electronic medical files. But their siloed storage systems don’t make it easy to share that data with each other, let alone with an artificial intelligence. Until that changes, AI won’t be curing the world of, well, probably anything.

Which is not to say AI in health care is all hype. Sure, Watson turned out to be less cancer-crushing computer prodigy and more very expensive electrical bill. But 2017 wasn’t all flops. In fact, this year saw artificial intelligence begin demonstrating real concrete usefulness inside exam rooms and out.

After a midnight flight across a time zone, the Jazz blew their biggest lead of the season. Following wacky plane trouble, the Bulls were blown out. The Magic faced travel issues thanks to inclement weather, then ate a loss hours later.

And one head coach had the audacity to utter the following after a loss: “It’s a little perplexing why we haven’t been more energetic on back-to-backs.”

Yeah, right …

These were just some details from the 10 teams that faced schedule alert scenarios in December — and nine of those teams lost, by the way. The only winner? The San Antonio Spurs, of course. But those savvy Spurs, ever mindful of fatigue and the pitfalls of the NBA schedule, barely won, as you’ll see below.

When most people hear the term “sports analytics,” the first thing that comes to mind is “Moneyball” — the 2004 book and 2011 film that detailed use of predictive analytics to evaluate on-field talent and find players with skills or other talents that competitors have overlooked. … We asked three MIT Sloan alumni to share their thoughts about emerging trends in sports analytics in the upcoming year, as well as the untold stories that don’t often make headlines.